ppi.infer.human: Inferring functionally related proteins using protein...

Description Usage Arguments Value Author(s) See Also Examples

View source: R/ppi.infer.human.R

Description

This function is designed for human protein-protein interaction from STRING database. Default format is 'hgnc'. The number of proteins is 10 in default. Note that the number of proteins used as a target may be different from the number of proteins in the input since mapping between formats is not always one-to-one in getBM.

Usage

1
2
ppi.infer.human(target, kernel, top = 10, classifier = net.infer,
  input = "hgnc_symbol", output = "hgnc_symbol", ...)

Arguments

target

set of interesting proteins or target class

kernel

the regularized Laplacian matrix for a graph

top

number of top proteins most closely related to target class (default: 10)

classifier

net.infer or net.infer.ST (default: net.infer)

input

input format

output

output format

...

additional parameters for the chosen classifier

Value

list

list of a target class used in the model

error

training error

CVerror

cross validation error, (when cross > 0 in net.infer)

top

top proteins

score

decision values for top proteins

Author(s)

Dongmin Jung, Xijin Ge

See Also

net.infer, net.infer.ST, getBM

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
# example 1
string.db.9606 <- STRINGdb$new(version='10',species=9606,
score_threshold = 999)
string.db.9606.graph <- string.db.9606$get_graph()
K.9606 <- net.kernel(string.db.9606.graph)
rownames(K.9606) <- substring(rownames(K.9606),6)
colnames(K.9606) <- substring(colnames(K.9606),6)
target <- colnames(K.9606)[1:100]
infer.human <- ppi.infer.human(target, K.9606,
input="ensembl_peptide_id")

## Not run: 
# example 2
library(graph)
data(apopGraph)
target <- nodes(apopGraph)
apoptosis.infer <- ppi.infer.human(target,K.9606,100)

# example 3
library(KEGGgraph)
library(KEGG.db)
pName <- "p53 signaling pathway"
pId <- mget(pName, KEGGPATHNAME2ID)[[1]]
getKGMLurl(pId, organism="hsa")
p53 <- system.file("extdata/hsa04115.xml",package="KEGGgraph")
p53graph <- parseKGML2Graph(p53,expandGenes=TRUE)

entrez <- translateKEGGID2GeneID(nodes(p53graph))
ensembl <- useMart("ensembl")
human.ensembl <- useDataset("hsapiens_gene_ensembl",mart=ensembl)
target <- getBM(attributes=c('entrezgene','hgnc_symbol'),
        filter='entrezgene', values=entrez,
        mart = human.ensembl)[,2]
p53.infer <- ppi.infer.human(target,K.9606,100)

## End(Not run)

PPInfer documentation built on Nov. 1, 2018, 2:10 a.m.